NVIDIA B200
NVIDIA's next-generation AI GPU based on the Blackwell architecture, designed to deliver up to 5x the training and 30x the inference performance of the H100.
The B200, announced in 2024 and shipping in 2025, represents NVIDIA's biggest generational leap in AI performance. It features two dies connected on a single package with up to 192GB of HBM3e memory. The chip's FP4 precision support enables dramatically faster inference for large language models. Pricing is expected to exceed $30,000 per chip. The B200 and its GB200 variant (combining B200 GPU with Grace CPU) are the primary targets for Big Tech's $650+ billion AI infrastructure investment in 2026. Thermal requirements essentially mandate liquid cooling for B200 deployments.
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Related Terms
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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